Solutions

Map/Reduce for Parallel Data Analysis

Without a doubt, data analytics have a powerful new tool with the "map/reduce" development model, which has recently surged in popularity as open source solutions such as Hadoop have helped raise awareness.

You may be surprised to learn that the map/reduce pattern dates back to pioneering work in the 1980s which originally demonstrated the power of data parallel computing. Having proven its value to accelerate "time to insight," map/reduce takes many forms and is now being offered in several competing frameworks.

If you are interested in adopting map/reduce within your organization, why not choose the easiest and best performing solution? ScaleOut StateServer’s in-memory data grid offers important advantages, such as industry-leading map/reduce performance and an extremely easy to use programming model that minimizes development time.

Ease of Use

ScaleOut StateServer's object-oriented framework makes it easy to access data and write map/reduce methods. There's no need to complicate your code by accessing file-based data (as is required by other approaches, such as Hadoop).

Map/reduce methods are written as if they are to be run on a single machine. This means that they are easier to write than other approaches, thanks to ScaleOut’s language integration with C#, Java, and C.

Language integrated query makes it a snap to specify the data you need to analyze.

No gurus are needed! Because of ScaleOut StateServer's automatic parallelism, no low-level or parallel programming skills are needed.

Keep What You Have

ScaleOut StateServer's data grid acts as an intelligent NoSQL layer on top of your existing relational database. This preserves your existing investment in long term data storage.

ScaleOut StateServer runs natively on all major platforms and interoperates across platforms and languages.

ScaleOut StateServer can seamlessly migrate your on-premise data grid to and from the cloud.